System and method for reducing similar photos for display and product design

ABSTRACT

A photo design smart assistant system for reducing similar photos for display and product design includes a similarity distance computation module that can calculate hash values of images and to calculate similarity distances between the images using at least the hash values, a burst grouping module that can automatically group the images into a burst based at least in part on the similarity distances of the images, wherein at least one image is automatically selected from the burst of images, an intelligent design creation engine that can automatically create a photo product design using the selected image from the burst, and a printing and finishing facility that can automatically make a physical photo product based on the photo product design.

CROSS-REFERENCE TO RELATED APPLICATION(S)

This application is a Continuation of U.S. application Ser. No.16/994,273, filed on Aug. 14, 2020, which is a Continuation-In-Part ofU.S. application Ser. No. 15/716,832, U.S. Pat. No. 10,762,126, filed onSep. 27, 2017, and issued on Sep. 1, 2020, the disclosures of which arehereby incorporated by reference in their entireties. To the extentappropriate, a claim of priority is made to each of the above-disclosedapplications.

TECHNICAL FIELD

This application relates to digital imaging technologies, and inparticular, to technologies that assist photo displays and photo productdesigns.

BACKGROUND OF THE INVENTION

With the advancement of consumer electronic devices, a vast number ofpictures are taken by mobile devices and digital cameras every day.Digital images can be viewed on devices, shared over computer networks,and incorporated into designs for photo products.

Handling large number of digital images is a challenge to photographers,and has become an obstacle to the images' utilizations. As mobile phonesand digital cameras have made photo taking very convenient, people oftensnap many pictures of the same scene at each moment especially if it isa special occasion. These pictures tend to be very similar to eachother, comprising the similar backgrounds with the same people havingsimilar facial expressions. The current image software usually displaysall the captured images on devices, which can be overwhelming forviewing, editing, and using in product design or electronic sharing.Users have to carefully compare these similar photos, remove most ofthem, and keep one or a few for display for each set of people at eachscene. Since people's facial expressions often only have minutedifferences between them, it is extremely hard to tell these photosapart even with examining at image magnifications. Trimming photos havebecome a time-consuming task for most photographers.

Similarly, photo products such as photobooks are often segmented by aseries of scenes and activities in an occasion or a story such as avacation, a trip, or a project. Typically, users only need one to a fewphotos for each set of people at each scene. Again, curating the photosin the context of a design style and layouts can be very time consuming.

There is therefore an immediate need for assisting photographers tosort, organize, and reduce numbers of photos to make their photoexperiences in viewing and creative designs more enjoyable and less timeconsuming.

SUMMARY OF THE INVENTION

The present application discloses a system and a method that cansignificantly enhance users' experiences associated with view photos anddesigning personalized photo products. Automated tools have beendeveloped to quantify the degree of similarities between photos andgroup the similar photos into bursts, each of which usually taken at asame moment at the same scene. The disclosed method groups images intoburst based on not only image time or locations, but also image content,and image context. Moreover, the disclosed method can effectivelyidentify both duplicate images (with different names and after editingand processing) as well as similar images. The automated tools thenselect one or a few best photos from each burst for display or forincorporating into an electronic publication or share, or into a photoproduct design.

The disclosed system and method can effectively simplify and declutterpresentations of photos on user devices. The disclosed system and methodcan drastically decrease the time and effort that users spend inmanaging, organizing, and utilizing their digital images. The processesof photo viewing, photo sharing, and photo design creation are made muchmore enjoyable. The disclosed system and method can be integrated withintelligent design tools for creating photo products and electronicsharing. In sum, the present invention can help people better preserveand share their precious memories.

In one general aspect, the present invention relates to acomputer-implemented method for reducing similar photos for display andproduct design, including calculating hash values of images by asimilarity distance computation module in a computer system; calculatingsimilarity distances between the images using at least the hash valuesby a similarity distance computation module in the computer system;automatically grouping the images into a burst based at least in part onthe similarity distances of the images by a burst grouping module in thecomputer system; automatically selecting at least one image from theburst of images; automatically creating a photo product design using theselected image from the burst by an intelligent design creation enginein the computer system; and automatically making a physical photoproduct based on the photo product design.

Implementations of the system may include one or more of the following.The step of calculating similarity distances can include automaticallycalculating a Hamming Distance between the hash values of the twoimages. The images having their Hamming Distance at or smaller than aburst threshold value can be grouped into a same burst by the burstgrouping module. The hash values can each has 64 bits, wherein the burstthreshold value can be 4. The step of calculating similarity distancescan further include calculating color densities, edge orientations, andedge densities between two images. The hash values of images can becalculated by a hashing method including pHash, Radial-Hash, orWavelet-based hash. The computer-implemented method can further includesequencing the images in a chronological sequence if the images haveavailable image times, wherein the similarity distances are calculatedbetween adjacent images in the chronological sequence. The images can begrouped into a burst by a burst grouping module based further on imagecapture times and image capture locations. The images can beautomatically grouped into the burst further based on imagecompositions, facial expressions, orientations of the faces, sizes ofthe faces, light exposures of the faces and persons in the image,importance of person(s) in the image, scene composition, dominantcolors, color histograms, block color histograms, and cloth colors andpatterns. At least one image can be automatically selected from theburst of images based on detection of faces, faces recognized presenceof faces of family and close friends, or positions and focus of thefaces in the image. The computer-implemented method can further includeautomatically selecting a product type, a product layout, or a productstyle for the photo product design by the intelligent design creationengine. The computer-implemented method can further includeautomatically selecting a product type, a product layout, or a productstyle for the photo product design based on a user's social data by theintelligent design creation engine. The computer-implemented method canfurther include companion images in a burst is automatically presentedby the intelligent design creation engine for user selection of adifferent image from the burst in the photo product design or the photodisplay. The computer-implemented method can further includeautomatically displaying the selected image from the burst on a deviceby the intelligent design creation engine. The computer-implementedmethod can further include designating a first image as a duplicateimage if a similarity distance between the first image and a secondimage is below a duplicate threshold value. The duplicate threshold issmaller than the burst threshold. The duplicate image can be discardedbefore the step of selecting. The computer-implemented method canfurther include designating a first image as a new image if a similaritydistance between the first image and other images is above the duplicatethreshold value.

In another general aspect, the present invention relates to a photodesign smart assistant system for reducing similar photos for displayand product design, which includes a similarity distance computationmodule that can calculate hash values of images and to calculatesimilarity distances between the images using at least the hash values;a burst grouping module that can automatically group the images into aburst based at least in part on the similarity distances of the images,wherein at least one image can automatically be selected from the burstof images; an intelligent design creation engine that can automaticallycreate a photo product design using the selected image from the burst;and a printing and finishing facility that can automatically make aphysical photo product based on the photo product design.

These and other aspects, their implementations and other features aredescribed in detail in the drawings, the description and the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of a network-based system for creating designsof personalized photo product and electronic sharing in accordance withthe present invention.

FIG. 2 is a block diagram of a smart assistant system that enables usersto create designs for photo products and electronic sharing inaccordance with some embodiments of the present invention.

FIG. 3A illustrate a reverse similarity distance between imagesprocessed by various operations from the same original image inaccordance with some embodiments of the present invention.

FIG. 3B further illustrates the reverse similarity distance betweenimages processed by various operations from the same original image inaccordance with some embodiments of the present invention.

FIG. 4 is a flow diagram for automatically quantifying similaritydistances between photos and grouping the photos based on the similaritydistances to be selected for photo utilizations in accordance with someembodiments of the present invention.

FIG. 5 illustrates bursts of similar photos at a user interface.

FIG. 6 illustrates a user interface showing a user's photos before andafter decluttering by the disclosed system and method.

FIG. 7 illustrates a user interface showing the automatic clustering ofphotos based on similarity calculation and automatic selections ofphotos to be placed in a photo product design in accordance with someembodiments of the present invention.

FIG. 8 is a flow diagram for removing duplicate photos based onsimilarity distances between photos before the photos are grouped andselected in accordance with some embodiments of the present invention.

DETAILED DESCRIPTION OF THE INVENTION

Referring to FIG. 1 , a network-based imaging service system 10, whichcan be operated by an image service provider such as Shutterfly, Inc.,includes a data center 30 and one or more product fulfillment centers40, 41 in communication with the data center 30 over a computer network80. The network-based imaging service system 10 allows users 70, 71 toorganize and share images via a wired network or a wireless network 51,create photo product designs, and order personalized photo productsbased on the product designs. The product fulfillment centers 40, 41manufacture and ship the ordered photo products to recipients.

The data center 30 includes one or more servers 32 configured tocommunicate with user devices 60, 61 operated by users 70, 71 on the webor via mobile applications, a data storage 34 for storing user data,image and design data, and product information, and computer processors36 for rendering images and product designs, analyzing and organizingimages, and analyzing and understanding user behaviors and preferences.

The users 70, 71 can view, edit, organize, and share images, and createdesigns and order personalized photo products using a mobile applicationor a browser by accessing the website. Images can also be uploaded froma mobile device 61 or a computer device 60 to the server 32 to allow theuser 70 and stored at the data center 30. The images or videos stored inthe data storage 34, the computer device 60, or the mobile device 61usually include photos or videos taken at different events andoccasions. If users 70, 71 are members of a family or a group (e.g. asoccer team), the images from the cameras 62, 63 and the mobile device61 can be grouped together to be incorporated into a photo product suchas a photobook, or used in a blog page for an event such as a soccergame.

The users 70, 71 can order physical products based on the photo productdesigns, which can be manufactured by the printing and finishingfacilities 40 and 41. The recipients receive the physical product withmessages from the users at locations 90, 95. The recipients can alsoreceive digital versions of the photo product designs over the Internet50 and/or a wireless network 51.

In the present disclosure, the term “personalized” (or “individualized”or “customized”) refers to content such as photos, text, designelements, layouts, or styles that is specific to a user, a recipient, agift product, or an occasion. A photo product can include a single pageor multiple pages. Each page can include one or more images, text, anddesign elements positioned in proportions in a particular layout.Examples of personalized photo products include photobooks, personalizedgreeting cards, photo stationeries, photographic prints, photo postersand photo banners, photo banners, photos on canvas, art prints, framedprints, photo decor, duvet, photo bags, photo playing cards, photoT-shirts, photo mugs, photo aprons, photo magnets, photo mouse pads,photo phone cases, tablet computer cases, photo key-chains, photocollectors, photo coasters, or other types of photo gifts or noveltyitems. Photobooks can be in the forms of image albums, scrapbooks, boundphoto calendars, or photo snap books, etc.

In some embodiments, referring to FIGS. 1 and 2 , a photo design smartassistant system 200 is provided to help users to organize their photos,and simplify their photo viewing and photo creations. The photo designsmart assistant system 200 includes a similarity distance computationmodule 210, a burst grouping module 220, a photo selection module 225,and an intelligent design creation engine 230.

As discussed in detail below, the similarity distance computation module210 is configured to calculate the similarity values between photos. Inthe present disclosure, the degree of similarity between photos can bequantified by a numerical distance, which can be used as a measure todefine and group photos that are similar. First, each image is hashed toobtain a hash value, which specifically represents the image. Next, asimilarity distance is calculated between two images using at least inpart the hash values of the two images. In one implementation, thesimilarity distance is obtained by calculating Hamming Distance betweenthe hash values of the two images.

This disclosed method and system are compatible with different hashingmethods can be implemented. For example, pHash, Radial-Hash,Wavelet-based hash, etc. pHash and other image-hash algorithms replacean image with an n-byte signature, which is almost unique. The signatureis usually derived from multi-resolution analysis of the image inquestion followed by statistical extraction of relevant pixel data, andis designed such that comparison of hash signatures is mathematicallytrivial. The feasibilities of different hash algorithms have been testedby analyzing the Hamming Distance between processed images obtained byprocessing a same original image with different operations such ascompression, resizing, enhancement, cropping, and rotation. FIG. 3Ashows reverse similarity distances are calculated between pairs ofimages obtained by processing a same image with different imageoperations. The reverse similarity distances are expressed in a scale of40 minus Hamming Distance between hash values obtained by a pHashalgorithm (a DCT based pHash algorithm). The results show thatsimilarity distances between different pairs of processed imagesoriginating from the same image remain small except for the rotatedimages. Since most of photographers' images taken in a burst maintainorientations throughout the burst series, the disclosed method is aneffective and low-computation tool to quantitatively measuresimilarities between images.

In another example, as shown in FIG. 3B, hash values are obtained fromprocessed images using a Radial-Hash algorithm. Reverse similaritydistances are calculated between pairs of images obtained by processinga same image with different image operations. The reverse similaritydistances are expressed in a scale of 40 minus Hamming Distance betweenthe hash values. Again, the results show that similarity distancesbetween different pairs of processed images originating from the sameimage remain small except for the rotated images.

In some embodiments, in addition to Hamming Distance of hash values, thesimilarity distances can also depend on differences in color densities,edge orientations, and edge densities between two images. The similaritydistance can be a function of the weighted differences in differentmetrics: Hamming Distance of hash values, differences in colordensities, edge orientations, and edge densities, etc.

Experiments employing the above described hashing and similaritydistance calculations have been shown to work well in automaticallyidentifying similar images.

The burst grouping module 220 sorts and groups photos into bursts basedon the numerical distances between photos obtained by the similaritydistance computation module 210. Specifically, photos having theirnumerical distance smaller than a predetermined threshold value can beconsidered as similar and are placed into a same burst. Photos havingtheir numerical distance larger than a predetermined threshold value areassigned to different bursts. For example, 64-bit hash values can becomputed from images using DCT-pHash. The maximum Hamming Distancebetween the hash values of different images is 64. The threshold can beset at 4, that is, images having Hamming Distance smaller or equal to 4are considered to be similar and belong to the same burst.

It should be noted that he disclosed method groups images into burstbased not only on image time or on image locations, but also based onimage content and image context. Moreover, the disclosed method caneffectively identify both duplicate images (with different names andafter editing and processing) as well as similar images that lookindiscernible to human eyes. For example, the threshold for thesimilarity distances between two images can be adjusted to identifyduplicate images specifically. For instance, a threshold of 2 forHamming Distance between hash values of two images can be used as acriterion for determining duplicate images.

Aside from the similarity distances, the grouping of image bursts canalso be based on image times and image capture locations. As describedabove, if available, image times can be used to sequence the imagesbefore calculating similarity distances. In most cases, similar imagesin a burst are taken within a short interval and at the same location.Then a maximum time limitation can be applied in the process ofidentifying images in a burst. Other criteria and metrics for groupingimages into bursts can include: positions and balance of the persons andobjects, locations of the faces, sizes of the faces, the importance ofthe persons in the image (based on the social data (stored in 260),scene composition, dominant colors, color histograms, block colorhistograms, and cloth colors and patterns of the persons in the image.

The photo selection module 225 is configured to select one, a couple of,or a few photos in each burst. The images are scored based on a fewcriteria: detection of faces, faces recognized, and the presence of VIPfaces (e.g. family and close friends) in the image; the estimated focusof the faces; face positions; image quality such as image focus, colorbalance, and light exposures. Image scoring can also depend on facialexpressions: for example, more preferred images include smiling faces,faces with open eyes, faces having moderate light exposures (rather thanover exposed or under exposed, etc.). The image selection can alsodepend on image quality parameter such as sharpness and light exposure.Images in a burst are then selected based on the image scores.

The photo design smart assistant system 200 also includes a product typelibrary 224, a product style library 226, and a product layout library228, which respectively stores types of events, product styles, andproduct layouts for personalized photo products and for electronicshares.

The intelligent design creation engine 230 can automatically display theselected photos from different bursts at a user interface on a device orautomatically incorporate the selected photos into a design for a photoproduct or for an electronic share. Moreover, the intelligent designcreation engine 230 creates the designs for photo products or forelectronic shares based also in part on the product types, the productlayouts, and the product styles respectively stored in the product typelibrary 224, the product style library 226, and the product layoutlibrary 228.

The product type library 224 stores the types of physical photo productssuch as photobooks, personalized greeting cards, photo stationeries . .. etc. described above. The product type library 224 can also storetypes of electronic shares such as digital images embedded on attachedto emails or text messages, blogs, pages on photo share sites or socialnetworks, chat applications for photo sharing, etc.

Product styles and product layouts include lists of pre-stored stylesand layouts, and can also include those styles and layouts that aredynamically generated by the photo design smart assistant system 200.

In the present disclosure, “product style” refers to product styleparameters such as a background pattern, a background photo, or abackground color, embellishments, tilt angles of the photos or photowells, the color scheme, or other design themes, characteristics, topicsor elements of a photo product. For photo products, product styles alsoinclude the level of designs and curation levels. The level of designsrefers to the extra design elements beyond photos incorporated in aphoto product. For example, at a low level of design, a photo book mayinclude mostly pictures and not much other design elements (e.g. a plainphoto book or photo card). The curation level refers to the degree ofselection and trimming of available photos in a photo collection relatedto the photo product. At a low curation level, all or most of the photostaken at an event can be used in a photo product such as a photo book.At a high curation level, only a small fraction of photos taken at anevent is selected to be incorporated by a photo product. Sometimes a setof style parameters can be formulated together to present a theme suchas fun, summer, modern, romantic, etc. Product style parameters aredefined and stored in the product style library 226.

In the present disclosure, “product layout” (including page layout)refers to product layout parameters such as the number, the density, thesizes, the positions of photos on one or two opposing pages, the gapsbetween the photos, and the margins between photos and the edges of apage. The product layout parameters can also include the presence orabsence, positions sizes, and font types of text and other designelements. Product layout parameters are defined and stored in theproduct layout library 228.

The photo design smart assistant system 200 can also include a userdatabase 240, an image store 250, and a social database 260. The userdatabase 240 stores user data such as account information, discountinformation, order information, relationship, and important datesassociated with users. The image store 250 stores users' photos or stockphotos managed by the online image service provider. The social database260 stores relationships (family members and friends) of a user, andface images and face models for the family members and the friends ofthe user.

The intelligent design creation engine 230 can create designs for photoproducts such as a product type, a product layout, or a product style,also based in part on the user data, and social data stored in the userdatabase 240 and the social database 260. or for electronic shares

The photo design smart assistant system 200 can be formed by the servers32, the data storage 34, and the processor 36 in the data center 30(FIG. 1 ), and can employ additional distributed computing equipment,including downloaded application on user devices (60, 61). In someembodiments, the photo design smart assistant system 200 can beimplemented in the cloud or with dedicated physical network equipment.

Referring next to FIG. 4 and FIGS. 1-2 , images that have availableimage times are sequenced in a chronological sequence (step 410) by acomputer system (e.g. the data center 30, user devices 60, 61, or theintelligent design creation engine 230). The available image times areusually the times at which the images are captured, but sometime caninclude upload times. For examples, images 500 at a user interface 510shown in FIG. 5 are sequenced chronologically based the associated imagetimes. The step 410 can be skipped for images whose image times areunknown.

Next, hash values of the images are calculated (step 420) by thesimilarity distance computation module 210. As described above, one ofseveral hash algorithms can be used to conduct this operation. Thesimilarity distances between images are calculated using at least thehash values (step 430) by the similarity distance computation module210. For example, Hamming Distance can be calculated between the hashvalues of the two images to obtain similarity value between the twoimages. For images that have available image times, the similaritydistances are calculated between adjacent images in the chronologicalsequence.

The adjacent images are then grouped into a burst based on at least thesimilarity distances of the associated adjacent images (step 440) by theburst grouping module 220. The images can be grouped into a burst if theassociated similarity distances of the images are below a burstthreshold value. FIG. 5 shows image bursts 520-523, each of whichincludes images taken rapidly at the same scene in a sequence. Theimages within each burst 520-523 are very similar to each other. Theimages can be taken by one or more devices, for example by familymembers who took pictures at the same event. As described above, thegrouping of image bursts can be based on several criteria such as imagequality, the image composition, significance of the persons in theimages, etc.

At least one image is automatically selected from the burst of images(step 450) by the burst grouping module 220. A photo display isautomatically created using the selected image from the burst (step 460)by the intelligent design creation engine 230. Referring to FIG. 6 , auser interface 600 includes images before the selections on the left andafter the selections on the right. Several bursts 610, 620, 630displayed on the left. Images 615, 625, 635 are selected respectivelyfrom the bursts 610, 620, 630 by the burst grouping module 220, and aredisplayed with other images on the right. As shown in FIG. 6 , theimages after selection are less cluttered than before the selections. Itshould be noted that FIG. 6 is used for illustration purpose. In realapplications, an image burst can include dozens of images; there can bea much bigger reduction in image numbers than illustrated in FIG. 6 .

A photo product design is automatically created using the selected imagefrom the burst (step 470) by the intelligent design creation engine 230.The intelligent product creation engine 230 can develop a design theme(e.g. fun, summer, modern, romantic, etc.) based user data and socialdata stored in the user database 240 and the social database 260. Theintelligent product creation engine 230 can also choose a product type,product layout parameters, the product style parameters usinginformation in the product type library 224, the product style library226, and the product layout library 228. As shown in FIG. 7 , a userinterface 700 includes an image collection 720, and a photo productdesign 720 automatically created by the intelligent design creationengine 230. The image collection 720 includes the bursts 610, 620, 630shown previously in FIG. 6 . The selected images 615, 625, 635 areautomatically placed in the photo product design 720 by the intelligentdesign creation engine 230. Optionally, the intelligent product creationengine 230 can receive user's edit command to revise the photo productdesign 720.

Optionally, companion images in a burst are automatically presented bythe intelligent design creation engine 230 for user selection of adifferent image from the burst in the photo product design or the photodisplay (step 480). Referring FIG. 7 , when a user selects or controls amouse over the image 635 in the photo product design 720, a window canappear to all show other images 636, 637 in the same burst 630. The userhas the option to compare these images and select a different image suchas the image 637 in the same burst 630. The same image change featurecan also be provided in a display of the selected images, such as to theright side of the user interface 600 in FIG. 6 .

After the photo product design 720 is finished, the user may order thecorresponding photo product to be made. The design data is transmittedfrom the data center 30 to a printing and finishing facility 40 or 41. Aphysical photo product is automatically manufactured based on the photoproduct design (step 490) at the printing and finishing facility 40 or41. Similarly, an electronic share of the selected images or the photoproduct design can be distributed over the network.

In some embodiments, the process illustrated in FIG. 4 is extendedfurther to include the removal of duplicated photos. Referring to FIG. 8, similar to step 420 (in FIG. 4 ), hash values of images are calculated(step 810). Then, similarity distances between the images are calculatedbased on at least the hash values (step 820), as described in relationto step 430 (in FIG. 4 ). If the similarity distance between a firstimage and a second image is below a duplicate threshold value, the firstimage is designated as a duplicate image (step 830) of the second image.The duplicate image (the first image or the second image) is discarded(step 840) in grouping photos into bursts and selecting the photos fromthe burst in steps 440-450. If the similarity distance between a firstimage and a second image is above the duplicate threshold value, thefirst image is designated as a new image (step 850). The first image isincluded in one of the bursts as described in steps 440-450 (step 860).The first image may be similar to the second image based on the criteriadiscussed above, and is assigned into a same group in step 440. Thefirst image may also be different enough based on the discussion aboveand is assigned to a different burst from the second image in step 440.If the first image is selected in step 450, it can be incorporated intoa photo product design (step 470) or in a photo display (step 460).

Using 64-bit hash values by DCT-pHash as an example, the maximum HammingDistance between the hash values of different images is 64. As discussedabove, the burst threshold for Hamming Distance for similar images canbe 4, that is, images having Hamming Distance smaller or equal to 4 areconsidered to be similar and assigned to the same burst. A duplicatethreshold for Hamming Distance for duplicate images can be 2, whichmeans that images having Hamming Distance smaller or equal to theduplicate threshold are designated as duplicate. Only one of them iskept while others discarded. The duplicate threshold is smaller than theburst threshold. The similarity distances are expressed in differentvalues in Radial-Hash and Wavelet-based hash. For example, the first andthe duplicate thresholds are in percentage values in Radial-Hash.

It should be understood that the presently disclosed systems and methodscan be compatible with different devices or applications other than theexamples described above. For example, the disclosed method is suitablefor desktop, tablet computers, mobile phones and other types of networkconnectable computer devices. The photo products compatible with thepresent invention are not limited to the examples described above.

1-29. (canceled)
 30. A system for reducing similar photos comprising: acomputing system comprising a processor, a similarity distancecomputation module, a burst grouping module, and a memorycommunicatively coupled to the processor, the memory storinginstructions executable by the processor to: calculate hash values ofimages, wherein the images include a first image and a second image;calculate similarity distances between the images using at least thehash values by the similarity distance computation module; automaticallygroup the images into a burst by the burst grouping module when thesimilarity distances of the images are below a burst threshold value;designate the first image as a duplicate image if a similarity distancebetween the first image and the second image is below a duplicatethreshold value, wherein the duplicate threshold value is smaller thanthe burst threshold value; and designate the first image as a new imageif similarity distances between the first image and other images in theburst are above the duplicate threshold value, wherein the new image isassigned to the burst when the similarity distances between the newimage and other images in the burst are below the burst threshold value.31. The system of claim 30, wherein the computing system furthercomprises a photo selection module, the system further comprisinginstructions executable by the processor to: automatically select atleast one image from the burst of images by the photo selection module.32. The system of claim 31, wherein the computing system furthercomprises an intelligent design creation engine, the system furthercomprising instructions executable by the processor to: automaticallycreate a photo product design using the selected image from the burst bythe intelligent design creation engine.
 33. The system of claim 31,further comprising instructions executable by the processor to: discardthe duplicate image before selecting at least one image from the burstof images.
 34. The system of claim 30, further comprising instructionsexecutable by the processor to: designate the first image as a new imageif similarity distances between the first image and other images in theburst are above the duplicate threshold value, wherein the new image isassigned to the burst when the similarity distances between the newimage and other images in the burst are below the burst threshold value.35. The system of claim 30, further comprising instructions executableby the processor to: sequence the images in a chronological sequence,wherein the similarity distances are calculated between adjacent imagesin the chronological sequence.
 36. The system of claim 30, wherein theimages are grouped into a burst based further on at least one of imagecapture times or image capture locations.
 37. The system of claim 30,wherein the images are automatically grouped into the burst furtherbased on image compositions, facial expressions, orientations of thefaces, sizes of the faces, light exposures of the faces and persons inthe image, importance of person(s) in the image, scene composition,dominant colors, color histograms, block color histograms, or clothcolors and patterns.
 38. The system of claim 31, wherein at least oneimage is automatically selected from the burst of images based ondetection of faces, faces recognized presence of faces of family andclose friends, or positions and focus of the faces in the image.
 39. Thesystem of claim 30, further comprising instructions executable by theprocessor to: automatically calculate a Hamming Distance between hashvalues of the first image and the second image to calculate similaritydistances between the images.
 40. The system of claim 39, wherein thehash values of the first image and the second image are calculated by ahashing method including pHash, Radial-Hash, or Wavelet-based hash. 41.The system of claim 32, further comprising instructions executable bythe processor to: automatically select a product type, a product layout,or a product style for the photo product design by the intelligentdesign creation engine.
 42. The system of claim 32, further comprisinginstructions executable by the processor to: automatically presentmultiple images in a burst by the intelligent design creation engine foruser selection of a different image from the burst in the photo productdesign or the photo display.
 43. The system of claim 32, furthercomprising instructions executable by the processor to: automaticallydisplay the selected image from the burst on a device by the intelligentdesign creation engine.
 44. A system for reducing similar photoscomprising: a computing system operating on a mobile device comprisingat least one processor communicatively connected to a memory, the memorystoring computer-executable instructions that when executed cause thefacial expression recognition system to: calculate hash values of imagesby a similarity distance computation module in the computer system,wherein the images includes a first image and a second image; calculatesimilarity distances between the images using at least the hash valuesby a similarity distance computation module in the computer systemconfigured to calculate differences in color densities, edgeorientations, or edge densities between the images; and automaticallygroup the images into a burst based at least in part on the similaritydistances of the images by a burst grouping module in the computersystem.
 45. The system of claim 44, further comprising instructionsexecutable by the processor to: automatically select at least one imagefrom the burst of images.
 46. The system of claim 45, further comprisinginstructions executable by the processor to: automatically create aphoto product design using the selected image from the burst by anintelligent design creation engine in the computer system.
 47. Thesystem of claim 44, wherein the images are grouped into the burst whenthe similarity distances of the images are below a burst thresholdvalue.
 48. The system of claim 45, further comprising instructionsexecutable by the processor to: discard the duplicate image beforeselecting at least one image from the burst of images.
 49. The system ofclaim 46, wherein the computing system further comprises a product typelibrary, a product style library, and a product layout library, andwherein the photo product design is based in part on product types,product styles, and product layouts stored in the product type library,the product style library, and the product layout library.